Self-organization theory states that collective patterns can emerge from the massive interaction of many units which behave according to simple rules. This theoretical framework has been applied to understand physical systems such as sand dunes and to animal groups including eusocial ants and bird flocks. The research program has been the same in these different fields: to unravel which simple and local individual behaviors produce the collective patterns observed in nature. Since the systems analyzed differ in many ways, the self-organization framework seems to be a very general and powerful tool to understand nature. In fact, treating, e.g., vertebrates and sand grains in the same way has led to relevant insights. However, this approach bears the risk of oversimplifying the story and of ignoring relevant information gathered in animal ecology and evolution during the last century. We propose that realizing these differences is crucial to improve our success on using the self-organization framework more properly and advance faster in our understanding of collective phenomena in vertebrates.
Results/Conclusions
Computer (individual-based) simulations have become a major tool to test hypotheses about how collective patterns in vertebrate systems emerge. However, some field studies have started to be done in the last years. We propose that this new data not only allow to improve the details of the models but to rethink the basics of the framework when applied to vertebrates. Specifically we identify three main emerging topics to be improved in the study of collective vertebrate patterns: the role of natural selection, the concept of de-coupling between individual and collective behaviour and its function, and the concept of what “simple behavior” means. We will present examples to demonstrate these topics.
